Most of the current quality assessment techniques interpret an image quality as a measure of its fidelity with another reference image, assuming the availability of that “perfect” image. It has been the concern of many researchers around the world to algorithmically assess the quality of image sequences based on human visual perception. This paper presents a novel technique for quantitatively assessing the quality of image sequences without the need for a reference image and in a way that precisely correlates to human judgement on quality. This research is a part of a larger framework that incorporates multi-objective optimisation algorithms to optimise the quality metrics of compressed videos acquired by autonomous vehicles and transmitted over low-bandwidth communication channels. Our system was trained on a dataset that involved 700 videos of 5 different categories. We validate the performance of our model and show that it highly correlates to the human subjective quality assessment.